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Forest law enforcement through district blacklisting in ... ... Forest law enforcement through district blacklisting in the Brazilian Amazon Elías Cisneros 1, Sophie Lian Zhou 2,

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  • Forest law enforcement through district blacklisting in the Brazilian Amazon

    Elías Cisneros1, Sophie Lian Zhou2, Jan Börner3

    1Center for Development Research - University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany.

    2Institute for Food and Resource Economics - University of Bonn, Nussallee 21, 53115 Bonn, Germany.

    3Center for Development Research - University of Bonn, and Center for International Forestry Research (CIFOR), Walter-Flex-Str. 3, 53113 Bonn, Germany. Corresponding author: [email protected]

    Abstract

    Deforestation in the Brazilian Amazon has dropped substantially after a peak at over 27 thousand square kilometers in 2004. Starting in 2008, the Brazilian Ministry of the Environment has regularly published blacklists of critical districts with high annual forest loss. Farms in blacklisted districts face stricter registration and environmental licensing rules. In this paper, we quantify the impact of blacklisting on deforestation. We first use spatial matching techniques using a large set of covariates to identify appropriate control districts. We then explore the effect of blacklisting on change in deforestation in double difference regression analyses using panel data covering the period from 2002-2012. Several robustness checks are conducted including an analysis of field-based enforcement missions as a potential causal mechanism behind the effectiveness of the blacklist. We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for in situ enforcement activities.

    Keywords: deforestation, impact evaluation, matching

    JEL codes: Q32, Q15, Q38, Q5, H43

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    1. Introduction

    Brazil stands out as one of the few countries in the world, where tropical deforestation rates have

    dropped over the past decade (Hansen et al. 2013). Emerging evidence from semi-experimental

    evaluation studies on the effectiveness of Brazil’s post-2004 strategy to combat Amazon

    deforestation unambiguously suggests that environmental policy has come to play a major role in

    determining land use decisions in the region (J. Assunção et al. 2012; CEPAL-IPEA-GIZ 2011;

    Hargrave and Kis-Katos 2013). Apart from a substantial expansion of the region’s protected area

    network, field-based law enforcement operations targeted to deforestation hot-spots by using

    improved remote sensing technologies have been among the major short-term success factors

    (Juliano Assunção et al. 2013a). Between late 2007 and early 2008, Brazil has introduced two

    additional measures to reinforce in situ enforcement action. Resolution 3.545 published in 2008

    by the Brazilian Monetary Council (Conselho Monetário Nacional) limits credit access for farms

    that are non-compliant with the Brazilian Forest Code and establishes best-practice rules for

    offenders to re-access credit flow. Assuncao et al. (Juliano Assunção et al. 2013b), estimate that

    this measure has avoided 2700 square kilometers of deforestation between 2009 and 2011. The

    Presidential Decree 6.321 (December 2007) created the legal basis for a list of priority

    municipalities, henceforth districts, with outstanding historical deforestation rates. In

    “blacklisted” districts, stricter rules with regard to the authorization of forest clearing applied and

    defined administrative targets (see details below) had to be fulfilled to qualify for removal from

    the list.

    Both decrees essentially operate as cross-compliance measures, where access to public services

    or administrative rights at farm or district level is made conditional on compliance with forest

    law. In this paper we apply semi-experimental evaluation techniques to gauge the role that

    district blacklisting has played in the overall contribution of Brazil’s policy mix to combat

    Amazon forest loss. We find that, on average, blacklisted districts have experienced distinctly

    larger reductions in deforestation than comparable non-listed districts and produce evidence that

    this difference is partially a genuine effect of blacklisting.

    The paper is structured as follows. Below, we describe key elements of the Brazilian blacklisting

    strategy. We also discuss the potential mechanisms and pathways through which blacklisting

    might have contributed to reducing deforestation beyond the combined effect of other policy

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    instruments (theory of change). Section 2 summarizes our empirical strategy to estimate the

    effect of blacklisting on deforestation. Section 3 documents our data sources and section 4

    presents main results and robustness checks. In section 5 we discuss potential caveats of our

    analysis in the context of the emerging literature evaluating conservation programs and section 6

    provides conclusions and implications for conservation policy design.

    History and impact logic of the Brazilian district blacklist

    Decree 6.321, published in December 2007, clearly defines the objective of the blacklist as a

    strategy to monitor and control illegal deforestation and prevent land degradation. It states that

    the list is to be updated annually based on official deforestation statistics and specifies the

    complementary roles of IBAMA and the National Institute for Agrarian Reform (INCRA) in

    monitoring and registering landholdings in the blacklisted districts. Three criteria are put forward

    as being used (without further specification) to compose the blacklist, namely:

    1. The total deforested area

    2. The total deforested area of the preceding three years

    3. The increase of deforestation of minimum three out of the past five years

    Figure 1 schematically depicts how the blacklist has evolved since the publication of Decree

    6.321.

    [Figure 1. History of district blacklisting and blacklist criteria. Positive numbers in parentheses depict additions to the blacklist. Negative numbers depict removals. ]

    In January 2008, the first blacklist was published covering 36 districts. Seven districts were

    added in each of the years 2009 and 2011. Only six districts were removed until 2012. Removal

    was conditioned on registering at least 80% of the eligible area (mostly privately claimed land)

    under the CAR. Moreover, annual deforestation had to be kept below 40 sqkm.

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    District blacklisting probably qualifies as the most innovative element in Brazil’s multi-

    instrument conservation policy mix. To our knowledge no other country has yet applied a similar

    institutional cross-compliance mechanism in the forestry sector. The impact pathway of

    blacklisting is still unclear and very little research on blacklisting as a governance mechanism

    exists. Jacobs and Anechiarico (1992) argue that contractor blacklisting is a sensible and

    ethically justifiable strategy to protect government organizations from fraud. China has

    experimented with an environmental disclosure policy including the publication of lists of

    violators of environmental regulations. A recent study found that this blacklisting strategy has

    helped in engaging civil society stakeholders in environmental governance (Tan 2014). The

    study, however, concluded that effects on behavioral change have been limited due to the

    country’s authoritarian structure. In 2010, a synthesis report by the Transparency and

    Accountability Initiative found that transparency and accountability policies have considerable

    potential to make a improve governance in sectors, such as public service delivery, natural

    resource governance, and donor aid (McGee and Gaventa 2010). Similar findings on public

    disclosure policies are

    2. Empirical Strategy

    The methodological challenge of evaluating the effect of the blacklist on deforestation in the

    blacklisted districts consists of identifying an appropriate counterfactual scenario of what would

    have happened in the absence of the blacklist (Khandker et al. 2010). From the previous section,

    we know that blacklisting was not random. Instead, regulators have used defined selection

    criteria that were all linked to historical deforestation. Regression Discontinuity Design (RDD) is

    a commonly used evaluation technique for interventions were the selection mechanism is known

    (Hahn et al. 2001). Unfortunately, the exact approach used to arrive at the published blacklists

    was never made public. Although past deforestation highly correlates with selection, it is not

    possible to reproduce the first list of 38 districts based on the three published selection criteria

    alone. We can thus only speculate, which other criteria could have played a role in composing

    the blacklist. Moreover, our sample of treated districts is too small for informative local linear

    regression analyses in an RDD.

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    A frequently used quasi-experimental evaluation technique in the presence of unknown selection

    mechanisms is matching (Andam et al. 2008; Gaveau et al. 2009; Ho et al. 2007; Honey-Rosés et

    al. 2011; Paul R. Rosenbaum and Rubin 1983). Matching relies on propensity scores or other

    distance measures that are derived from observed characteristics of treated and non-treated

    observations (here districts).